{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3c5cea41",
   "metadata": {},
   "source": [
    "## Diving into continuous futures data with Nasdaq Data Link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "357faf65",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nasdaqdatalink\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c524b6cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "nasdaqdatalink.ApiConfig.api_key = \"YOUR_API_KEY\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af9301ba",
   "metadata": {},
   "source": [
    "Fetches data for the specified NASDAQ dataset and stores it in 'data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e68ee062",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = nasdaqdatalink.get(\"CHRIS/CME_ES1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "201533fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-09-09</th>\n",
       "      <td>934.00</td>\n",
       "      <td>942.00</td>\n",
       "      <td>933.00</td>\n",
       "      <td>934.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>934.00</td>\n",
       "      <td>7034.0</td>\n",
       "      <td>1109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-10</th>\n",
       "      <td>934.00</td>\n",
       "      <td>935.00</td>\n",
       "      <td>915.00</td>\n",
       "      <td>915.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>915.00</td>\n",
       "      <td>11387.0</td>\n",
       "      <td>2325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-11</th>\n",
       "      <td>916.00</td>\n",
       "      <td>918.00</td>\n",
       "      <td>900.00</td>\n",
       "      <td>908.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>908.00</td>\n",
       "      <td>2523.0</td>\n",
       "      <td>2549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-12</th>\n",
       "      <td>908.00</td>\n",
       "      <td>926.00</td>\n",
       "      <td>904.00</td>\n",
       "      <td>924.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>924.00</td>\n",
       "      <td>928.0</td>\n",
       "      <td>2163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-15</th>\n",
       "      <td>925.00</td>\n",
       "      <td>930.00</td>\n",
       "      <td>920.00</td>\n",
       "      <td>922.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>922.00</td>\n",
       "      <td>208.0</td>\n",
       "      <td>2107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-14</th>\n",
       "      <td>4248.00</td>\n",
       "      <td>4257.50</td>\n",
       "      <td>4233.50</td>\n",
       "      <td>4255.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>4254.75</td>\n",
       "      <td>1089658.0</td>\n",
       "      <td>1833460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-15</th>\n",
       "      <td>4255.25</td>\n",
       "      <td>4267.50</td>\n",
       "      <td>4238.00</td>\n",
       "      <td>4247.25</td>\n",
       "      <td>-8.25</td>\n",
       "      <td>4246.50</td>\n",
       "      <td>944668.0</td>\n",
       "      <td>1265301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16</th>\n",
       "      <td>4248.00</td>\n",
       "      <td>4251.25</td>\n",
       "      <td>4200.75</td>\n",
       "      <td>4212.75</td>\n",
       "      <td>-23.50</td>\n",
       "      <td>4223.00</td>\n",
       "      <td>482760.0</td>\n",
       "      <td>744020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-17</th>\n",
       "      <td>4214.25</td>\n",
       "      <td>4232.50</td>\n",
       "      <td>4193.00</td>\n",
       "      <td>4225.75</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>4222.25</td>\n",
       "      <td>305144.0</td>\n",
       "      <td>590729.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-18</th>\n",
       "      <td>4225.75</td>\n",
       "      <td>4231.00</td>\n",
       "      <td>4183.75</td>\n",
       "      <td>4185.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4187.25</td>\n",
       "      <td>35117.0</td>\n",
       "      <td>539808.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6069 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low     Last  Change   Settle     Volume  \\\n",
       "Date                                                                         \n",
       "1997-09-09   934.00   942.00   933.00   934.00     NaN   934.00     7034.0   \n",
       "1997-09-10   934.00   935.00   915.00   915.00     NaN   915.00    11387.0   \n",
       "1997-09-11   916.00   918.00   900.00   908.00     NaN   908.00     2523.0   \n",
       "1997-09-12   908.00   926.00   904.00   924.00     NaN   924.00      928.0   \n",
       "1997-09-15   925.00   930.00   920.00   922.00     NaN   922.00      208.0   \n",
       "...             ...      ...      ...      ...     ...      ...        ...   \n",
       "2021-06-14  4248.00  4257.50  4233.50  4255.00    9.00  4254.75  1089658.0   \n",
       "2021-06-15  4255.25  4267.50  4238.00  4247.25   -8.25  4246.50   944668.0   \n",
       "2021-06-16  4248.00  4251.25  4200.75  4212.75  -23.50  4223.00   482760.0   \n",
       "2021-06-17  4214.25  4232.50  4193.00  4225.75   -0.75  4222.25   305144.0   \n",
       "2021-06-18  4225.75  4231.00  4183.75  4185.50     NaN  4187.25    35117.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "1997-09-09                      1109.0  \n",
       "1997-09-10                      2325.0  \n",
       "1997-09-11                      2549.0  \n",
       "1997-09-12                      2163.0  \n",
       "1997-09-15                      2107.0  \n",
       "...                                ...  \n",
       "2021-06-14                   1833460.0  \n",
       "2021-06-15                   1265301.0  \n",
       "2021-06-16                    744020.0  \n",
       "2021-06-17                    590729.0  \n",
       "2021-06-18                    539808.0  \n",
       "\n",
       "[6069 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118ab372",
   "metadata": {},
   "source": [
    "Fetches the data in numpy array format for the specified dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac0c824d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = nasdaqdatalink.get(\"CHRIS/CME_ES1\", returns=\"numpy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d5acf8fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([('1997-09-09T00:00:00.000000000',  934.  ,  942.  ,  933.  ,  934.  ,    nan,  934.  ,   7034.,   1109.),\n",
       "           ('1997-09-10T00:00:00.000000000',  934.  ,  935.  ,  915.  ,  915.  ,    nan,  915.  ,  11387.,   2325.),\n",
       "           ('1997-09-11T00:00:00.000000000',  916.  ,  918.  ,  900.  ,  908.  ,    nan,  908.  ,   2523.,   2549.),\n",
       "           ...,\n",
       "           ('2021-06-16T00:00:00.000000000', 4248.  , 4251.25, 4200.75, 4212.75, -23.5 , 4223.  , 482760., 744020.),\n",
       "           ('2021-06-17T00:00:00.000000000', 4214.25, 4232.5 , 4193.  , 4225.75,  -0.75, 4222.25, 305144., 590729.),\n",
       "           ('2021-06-18T00:00:00.000000000', 4225.75, 4231.  , 4183.75, 4185.5 ,    nan, 4187.25,  35117., 539808.)],\n",
       "          dtype=[('Date', '<M8[ns]'), ('Open', '<f8'), ('High', '<f8'), ('Low', '<f8'), ('Last', '<f8'), ('Change', '<f8'), ('Settle', '<f8'), ('Volume', '<f8'), ('Previous Day Open Interest', '<f8')])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a7887d8",
   "metadata": {},
   "source": [
    "Fetches data for the specified dataset within the given date range and stores it in 'data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "685c7b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = nasdaqdatalink.get(\n",
    "    \"CHRIS/CME_ES1\", start_date=\"2001-12-31\", end_date=\"2005-12-31\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "859d7be0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-12-31</th>\n",
       "      <td>1159.25</td>\n",
       "      <td>1163.00</td>\n",
       "      <td>1141.25</td>\n",
       "      <td>1149.25</td>\n",
       "      <td>None</td>\n",
       "      <td>1149.25</td>\n",
       "      <td>96049.0</td>\n",
       "      <td>55442.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-02</th>\n",
       "      <td>1143.75</td>\n",
       "      <td>1156.25</td>\n",
       "      <td>1136.50</td>\n",
       "      <td>1154.00</td>\n",
       "      <td>None</td>\n",
       "      <td>1154.00</td>\n",
       "      <td>216814.0</td>\n",
       "      <td>60568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-03</th>\n",
       "      <td>1154.00</td>\n",
       "      <td>1168.00</td>\n",
       "      <td>1150.75</td>\n",
       "      <td>1166.50</td>\n",
       "      <td>None</td>\n",
       "      <td>1166.50</td>\n",
       "      <td>209639.0</td>\n",
       "      <td>66239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-04</th>\n",
       "      <td>1167.00</td>\n",
       "      <td>1178.25</td>\n",
       "      <td>1163.75</td>\n",
       "      <td>1175.25</td>\n",
       "      <td>None</td>\n",
       "      <td>1175.25</td>\n",
       "      <td>217254.0</td>\n",
       "      <td>72023.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-07</th>\n",
       "      <td>1173.25</td>\n",
       "      <td>1178.50</td>\n",
       "      <td>1163.75</td>\n",
       "      <td>1166.75</td>\n",
       "      <td>None</td>\n",
       "      <td>1166.75</td>\n",
       "      <td>252385.0</td>\n",
       "      <td>73511.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-23</th>\n",
       "      <td>1275.75</td>\n",
       "      <td>1277.75</td>\n",
       "      <td>1273.00</td>\n",
       "      <td>1276.50</td>\n",
       "      <td>None</td>\n",
       "      <td>1276.50</td>\n",
       "      <td>274193.0</td>\n",
       "      <td>1042598.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-27</th>\n",
       "      <td>1277.00</td>\n",
       "      <td>1281.00</td>\n",
       "      <td>1262.50</td>\n",
       "      <td>1264.00</td>\n",
       "      <td>None</td>\n",
       "      <td>1264.00</td>\n",
       "      <td>593885.0</td>\n",
       "      <td>1037733.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-28</th>\n",
       "      <td>1263.50</td>\n",
       "      <td>1268.25</td>\n",
       "      <td>1263.00</td>\n",
       "      <td>1265.25</td>\n",
       "      <td>None</td>\n",
       "      <td>1265.25</td>\n",
       "      <td>391511.0</td>\n",
       "      <td>1047344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-29</th>\n",
       "      <td>1265.25</td>\n",
       "      <td>1267.75</td>\n",
       "      <td>1258.50</td>\n",
       "      <td>1260.00</td>\n",
       "      <td>None</td>\n",
       "      <td>1260.00</td>\n",
       "      <td>367199.0</td>\n",
       "      <td>1054788.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-30</th>\n",
       "      <td>1260.25</td>\n",
       "      <td>1261.50</td>\n",
       "      <td>1251.25</td>\n",
       "      <td>1254.75</td>\n",
       "      <td>None</td>\n",
       "      <td>1254.75</td>\n",
       "      <td>463621.0</td>\n",
       "      <td>1000442.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1015 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low     Last Change   Settle    Volume  \\\n",
       "Date                                                                       \n",
       "2001-12-31  1159.25  1163.00  1141.25  1149.25   None  1149.25   96049.0   \n",
       "2002-01-02  1143.75  1156.25  1136.50  1154.00   None  1154.00  216814.0   \n",
       "2002-01-03  1154.00  1168.00  1150.75  1166.50   None  1166.50  209639.0   \n",
       "2002-01-04  1167.00  1178.25  1163.75  1175.25   None  1175.25  217254.0   \n",
       "2002-01-07  1173.25  1178.50  1163.75  1166.75   None  1166.75  252385.0   \n",
       "...             ...      ...      ...      ...    ...      ...       ...   \n",
       "2005-12-23  1275.75  1277.75  1273.00  1276.50   None  1276.50  274193.0   \n",
       "2005-12-27  1277.00  1281.00  1262.50  1264.00   None  1264.00  593885.0   \n",
       "2005-12-28  1263.50  1268.25  1263.00  1265.25   None  1265.25  391511.0   \n",
       "2005-12-29  1265.25  1267.75  1258.50  1260.00   None  1260.00  367199.0   \n",
       "2005-12-30  1260.25  1261.50  1251.25  1254.75   None  1254.75  463621.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "2001-12-31                     55442.0  \n",
       "2002-01-02                     60568.0  \n",
       "2002-01-03                     66239.0  \n",
       "2002-01-04                     72023.0  \n",
       "2002-01-07                     73511.0  \n",
       "...                                ...  \n",
       "2005-12-23                   1042598.0  \n",
       "2005-12-27                   1037733.0  \n",
       "2005-12-28                   1047344.0  \n",
       "2005-12-29                   1054788.0  \n",
       "2005-12-30                   1000442.0  \n",
       "\n",
       "[1015 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5efe1e57",
   "metadata": {},
   "source": [
    "Fetches data for a specific column in the dataset within the given date range and stores it in 'data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3cbba229",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = nasdaqdatalink.get(\n",
    "    \"CHRIS/CME_ES1.8\", start_date=\"2001-12-31\", end_date=\"2005-12-31\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b006e03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Previous Day Open Interest</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "      <th>2001-12-31</th>\n",
       "      <td>55442.0</td>\n",
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       "    <tr>\n",
       "      <th>2002-01-02</th>\n",
       "      <td>60568.0</td>\n",
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       "    <tr>\n",
       "      <th>2002-01-03</th>\n",
       "      <td>66239.0</td>\n",
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       "      <th>2002-01-04</th>\n",
       "      <td>72023.0</td>\n",
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       "      <th>2002-01-07</th>\n",
       "      <td>73511.0</td>\n",
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       "      <th>...</th>\n",
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       "      <th>2005-12-23</th>\n",
       "      <td>1042598.0</td>\n",
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       "    <tr>\n",
       "      <th>2005-12-27</th>\n",
       "      <td>1037733.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-28</th>\n",
       "      <td>1047344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-29</th>\n",
       "      <td>1054788.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-30</th>\n",
       "      <td>1000442.0</td>\n",
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       "            Previous Day Open Interest\n",
       "Date                                  \n",
       "2001-12-31                     55442.0\n",
       "2002-01-02                     60568.0\n",
       "2002-01-03                     66239.0\n",
       "2002-01-04                     72023.0\n",
       "2002-01-07                     73511.0\n",
       "...                                ...\n",
       "2005-12-23                   1042598.0\n",
       "2005-12-27                   1037733.0\n",
       "2005-12-28                   1047344.0\n",
       "2005-12-29                   1054788.0\n",
       "2005-12-30                   1000442.0\n",
       "\n",
       "[1015 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "display(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "479f0d0e",
   "metadata": {},
   "source": [
    "Fetches data for the specified dataset with monthly aggregation and stores it in 'data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50232cd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = nasdaqdatalink.get(\"CHRIS/CME_ES1\", collapse=\"monthly\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dc1604d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-09-30</th>\n",
       "      <td>961.00</td>\n",
       "      <td>965.25</td>\n",
       "      <td>952.75</td>\n",
       "      <td>954.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>954.50</td>\n",
       "      <td>29254.0</td>\n",
       "      <td>5864.0</td>\n",
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       "    <tr>\n",
       "      <th>1997-10-31</th>\n",
       "      <td>902.00</td>\n",
       "      <td>926.25</td>\n",
       "      <td>889.75</td>\n",
       "      <td>924.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>924.00</td>\n",
       "      <td>25784.0</td>\n",
       "      <td>14863.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-11-30</th>\n",
       "      <td>954.75</td>\n",
       "      <td>962.25</td>\n",
       "      <td>953.75</td>\n",
       "      <td>955.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>955.00</td>\n",
       "      <td>12532.0</td>\n",
       "      <td>16750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-12-31</th>\n",
       "      <td>979.50</td>\n",
       "      <td>984.00</td>\n",
       "      <td>975.25</td>\n",
       "      <td>979.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>979.00</td>\n",
       "      <td>9914.0</td>\n",
       "      <td>5968.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-31</th>\n",
       "      <td>987.75</td>\n",
       "      <td>993.50</td>\n",
       "      <td>984.00</td>\n",
       "      <td>987.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>987.75</td>\n",
       "      <td>26001.0</td>\n",
       "      <td>11788.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>2021-02-28</th>\n",
       "      <td>3823.25</td>\n",
       "      <td>3858.50</td>\n",
       "      <td>3785.00</td>\n",
       "      <td>3807.75</td>\n",
       "      <td>-18.75</td>\n",
       "      <td>3809.25</td>\n",
       "      <td>2891634.0</td>\n",
       "      <td>2656039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>3949.50</td>\n",
       "      <td>3983.75</td>\n",
       "      <td>3941.50</td>\n",
       "      <td>3966.25</td>\n",
       "      <td>19.75</td>\n",
       "      <td>3967.50</td>\n",
       "      <td>1565025.0</td>\n",
       "      <td>2558591.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>4200.75</td>\n",
       "      <td>4201.50</td>\n",
       "      <td>4167.25</td>\n",
       "      <td>4180.50</td>\n",
       "      <td>-29.00</td>\n",
       "      <td>4174.50</td>\n",
       "      <td>1849796.0</td>\n",
       "      <td>2657040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>4211.25</td>\n",
       "      <td>4217.50</td>\n",
       "      <td>4201.00</td>\n",
       "      <td>4203.25</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4202.50</td>\n",
       "      <td>1035552.0</td>\n",
       "      <td>2599181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>4225.75</td>\n",
       "      <td>4231.00</td>\n",
       "      <td>4183.75</td>\n",
       "      <td>4185.50</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>4187.25</td>\n",
       "      <td>35117.0</td>\n",
       "      <td>539808.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>286 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low     Last  Change   Settle     Volume  \\\n",
       "Date                                                                         \n",
       "1997-09-30   961.00   965.25   952.75   954.50     NaN   954.50    29254.0   \n",
       "1997-10-31   902.00   926.25   889.75   924.00     NaN   924.00    25784.0   \n",
       "1997-11-30   954.75   962.25   953.75   955.00     NaN   955.00    12532.0   \n",
       "1997-12-31   979.50   984.00   975.25   979.00     NaN   979.00     9914.0   \n",
       "1998-01-31   987.75   993.50   984.00   987.75     NaN   987.75    26001.0   \n",
       "...             ...      ...      ...      ...     ...      ...        ...   \n",
       "2021-02-28  3823.25  3858.50  3785.00  3807.75  -18.75  3809.25  2891634.0   \n",
       "2021-03-31  3949.50  3983.75  3941.50  3966.25   19.75  3967.50  1565025.0   \n",
       "2021-04-30  4200.75  4201.50  4167.25  4180.50  -29.00  4174.50  1849796.0   \n",
       "2021-05-31  4211.25  4217.50  4201.00  4203.25    3.50  4202.50  1035552.0   \n",
       "2021-06-30  4225.75  4231.00  4183.75  4185.50   -0.75  4187.25    35117.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "1997-09-30                      5864.0  \n",
       "1997-10-31                     14863.0  \n",
       "1997-11-30                     16750.0  \n",
       "1997-12-31                      5968.0  \n",
       "1998-01-31                     11788.0  \n",
       "...                                ...  \n",
       "2021-02-28                   2656039.0  \n",
       "2021-03-31                   2558591.0  \n",
       "2021-04-30                   2657040.0  \n",
       "2021-05-31                   2599181.0  \n",
       "2021-06-30                    539808.0  \n",
       "\n",
       "[286 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d98ba3",
   "metadata": {},
   "source": [
    "Defines a list of contract codes to fetch data for multiple contracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce8dfd42",
   "metadata": {},
   "outputs": [],
   "source": [
    "contracts = [\n",
    "    \"CHRIS/CME_ES1.6\",\n",
    "    \"CHRIS/CME_ES2.6\",\n",
    "    \"CHRIS/CME_ES3.6\",\n",
    "    \"CHRIS/CME_ES4.6\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4645eb7e",
   "metadata": {},
   "source": [
    "Fetches data for the specified contracts within the given date range and stores it in 'data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4d36af7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = nasdaqdatalink.get(contracts, start_date=\"2015-01-01\", end_date=\"2015-12-31\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e563ffc5",
   "metadata": {},
   "source": [
    "Plots the data for the first contract on the first available date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "62c398b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'ES on 2015-01-02 00:00:00'}>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.iloc[0].plot(title=f\"ES on {data.index[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34765d3c",
   "metadata": {},
   "source": [
    "**Jason Strimpel** is the founder of <a href='https://pyquantnews.com/'>PyQuant News</a> and co-founder of <a href='https://www.tradeblotter.io/'>Trade Blotter</a>. His career in algorithmic trading spans 20+ years. He previously traded for a Chicago-based hedge fund, was a risk manager at JPMorgan, and managed production risk technology for an energy derivatives trading firm in London. In Singapore, he served as APAC CIO for an agricultural trading firm and built the data science team for a global metals trading firm. Jason holds degrees in Finance and Economics and a Master's in Quantitative Finance from the Illinois Institute of Technology. His career spans America, Europe, and Asia. He shares his expertise through the <a href='https://pyquantnews.com/subscribe-to-the-pyquant-newsletter/'>PyQuant Newsletter</a>, social media, and has taught over 1,000+ algorithmic trading with Python in his popular course **<a href='https://gettingstartedwithpythonforquantfinance.com/'>Getting Started With Python for Quant Finance</a>**. All code is for educational purposes only. Nothing provided here is financial advise. Use at your own risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0b235a0-e23e-453c-8519-5e72489a1571",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
